User Participation Behavior in Crowdsourcing Platforms: Impact of Information Signaling Theory - MDPI
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sustainability
Article
User Participation Behavior in Crowdsourcing Platforms:
Impact of Information Signaling Theory
Suying Gao, Xiangshan Jin * and Ye Zhang
School of Economics and Management, Hebei University of Technology, Tianjin 300401, China;
suyingGAO1@aliyun.com (S.G.); yehest@126.com (Y.Z.)
* Correspondence: king1227278527@163.com
Abstract: As a type of open innovation, emerging crowdsourcing platforms have garnered significant
attention from users and companies. This study aims to determine how online seeker signals affect the
user participation behavior of the solver in the open innovation crowdsourcing community, by means
of which to achieve the long-term sustainable development of the emerging crowdsourcing platform.
We performed data analysis based on the system of regression equation approach in order to conduct
quantitative research. We found that online reputation and salary comparison positively influence
user participation behavior, and that interpersonal trust acts as a strong mediator in the relationship
between salary comparison and user participation behavior. In addition, we observed an elevation
in task information diversification as a moderator, which positively affects online seeker signals
on user participation behavior. Furthermore, an upsurge was noted in task information overload
as a moderator, which adversely affects online seeker signals on user participation behavior. The
contributions of this article include the application of the innovative signal transmission model, and
online task information quality has important guiding significance on how to design task descriptions
for emerging crowdsourcing platforms in order to stimulate user participation behavior.
Citation: Gao, S.; Jin, X.; Zhang, Y. Keywords: open innovation; emerging crowdsourcing platform; sustainable development; signaling;
User Participation Behavior in interpersonal trust; user participation behavior; online task information quality
Crowdsourcing Platforms: Impact of
Information Signaling Theory.
Sustainability 2021, 13, 6290.
https://doi.org/10.3390/su13116290 1. Introduction
The rapid advancement of the service economy has created an environment where
Academic Editor: Andrea Pérez
a company’s product development and design are no longer restricted to its own inno-
vation. Today, a growing number of companies are using the Internet to acquire new
Received: 21 April 2021
Accepted: 31 May 2021
key knowledge from the outside world [1], and users are progressively transformed into
Published: 2 June 2021
value co-creators for the company [2]. Crowdsourcing platforms are means that use the
Internet to provide external knowledge to companies for open innovation [3]. The term
Publisher’s Note: MDPI stays neutral
“crowdsourcing” was first proposed by the American journalist Jeff Howe [4]. Owing to
with regard to jurisdictional claims in
the advancement of modern information technology, crowdsourcing platforms offer the
published maps and institutional affil- advantages of low costs and multiple channels, which facilitate multiple users to resolve
iations. research and development issues, program design, and other innovative tasks through
crowdsourcing platforms like Innocentive, ZBJ.COM, and Taskcn.com.
Domestic open innovation is prevalent on several platforms, among which the leading
and most valuable is the crowdsourcing platform. The domestic crowdsourcing platform
Copyright: © 2021 by the authors.
has numerous seekers and tasks. The seeker could be a company or an individual. Although
Licensee MDPI, Basel, Switzerland.
transactions are frequent, if the solvers’ participation is insufficient, the final result could
This article is an open access article
be undesirable. For example, many domestic crowdsourcing websites, such as Taskcn.com,
distributed under the terms and cannot attract enough solvers to participate in the tasks, resulting in the unsustainable
conditions of the Creative Commons development of the platform, and these sites currently face many problems. While the
Attribution (CC BY) license (https:// seeker releases significant information, the solver has inadequate motivation to participate
creativecommons.org/licenses/by/ in the task. Previous scholars’ research on user participation behavior mainly focused on
4.0/). participation intentions [5] and continuous participation intentions [6,7]. Few scholars
Sustainability 2021, 13, 6290. https://doi.org/10.3390/su13116290 https://www.mdpi.com/journal/sustainabilitySustainability 2021, 13, 6290 2 of 19
can really use objective data to measure which variables can objectively affect their users’
participation behavior. Therefore, there is a large gap between previous research and
actual user participation behavior, which has certain limitations. This article aims to crawl
the objective data of the webpage through Python, and then to objectively measure user
participation behavior, in order to make up for the research gap.
The biggest concern for companies and users is the presence of information asymmetry
between one another. In order to resolve the problem of information asymmetry, a solution
is proposed using signaling theory. Signaling theory is principally about decreasing the
asymmetry of information between two parties [8]. Trust is acknowledged in theoretical
research as a vital factor in user participation in the platform [9]. In addition, we aim to
help users to discover more tasks of interest to them and increase their involvement in the
tasks. In turn, the company can have more selections to choose the best one from. The
literature review reveals that research into the trust mechanism is primarily focused on the
e-commerce industry and platforms [10,11]. Crowdsourcing platforms could be regarded
as a typical example of e-commerce. Meanwhile, it is obvious that seekers and solvers
need to build trust before knowledge transactions, and so we should focus on how to build
trust between seeker and solver. Thus, the signaling theory between them can also be cited.
Signaling theory helps to reduce uncertainty, so that the solver can make better decisions
about whether to participate in tasks. When resolving the existing information asymmetry
issue and endorsing the exchange, providers can mark the quality of their products or
services using indicators like price, guarantee, or reputation. In our context, the seeker
aims to use signaling theory to make more solvers focus on their tasks and be willing to
participate in the tasks so that both parties can reach an exchange or cooperation. Taking
signaling theory as a theoretical perspective, we used seeker reputation and task price as
the design mechanism to decrease information asymmetry. In addition, we examined the
effects of different design mechanisms on solvers’ participation, and used interpersonal
trust as a mediating variable to investigate whether different design mechanisms should
use a mediation to affect the seekers’ participation. Furthermore, different effects occur in
different situations; for example, the introduction of a company’s product will not affect
the users’ behavior; however, it will affect the correlation between design mechanism and
behavior. Hence, we assume that online task information quality will act as a moderator
variable to affect the influence of the design mechanism on the solvers’ participation.
The research deductions have a significant contribution to both theory and practice.
We applied signaling theory to the context of open innovation, escalating the boundaries
of theoretical application. Meanwhile, we positioned the dependent variable as user
participation behavior in the context of open innovation. Thus, this study concentrates
more on the actual participation behavior of users, rather than their intention to participate.
Regarding practice, this can help the seeker to attract more users to participate, and assist
the solver in establishing trust and understanding of the seeker to protect their rights
and interests. Moreover, this can help to enhance the platform management mechanism,
including the trust mechanism. Therefore, the platform can develop more sustainably.
Primarily based on the theoretical perspective of signaling theory, this study uses
online seeker signals to assess user participation behavior. Meanwhile, we explored the
roles of task information diversification and task information overload in moderating the
correlation. Section 2 presents the theoretical basis and hypothesis proposed; Section 3
presents methods; Section 4 presents empirical models and data results; and Section 5
provides discussion.
2. Theoretical Background and Hypotheses
2.1. Theoretical Background
2.1.1. Crowdsourcing Platforms
The crowdsourcing platform is a creation of open innovation, and the online crowd-
sourcing community has become a critical source of innovation and knowledge dissemi-
nation, enabling the online public to give full play to their talents [12]. Some papers areSustainability 2021, 13, 6290 3 of 19
based on the form of tasks, and whether or not monetary compensation is present, dividing
crowdsourcing platforms into three categories: virtual labor markets (VLMs), tournament
crowdsourcing (TC), and open collaboration (OC) [13]. VLMs are an IT-mediated market,
wherein the solver can complete online services anywhere and exchange microtasking for
monetary compensation [14]. TC is another type of crowdsourcing with monetary com-
pensation. On the IT-mediated crowdsourcing platform Kaggle, or an internal platform
such as Challenge.gov, the seeker holds a competition to publish its mission and devises
rules and rewards for the game, and then the solver who wins the competition receives a
reward [15]. In the OC model, organizations publish issues to the platform through the
IT system, and the public participates voluntarily, with no monetary compensation. Such
crowdsourcing platforms include Wikipedia, or using social media and online communities
to get contributions [16].
In this study, the crowdsourcing platform has several features. First, it is an online
network service trading platform for microtask crowdsourcing in the Chinese context.
Microtasking comprises design, development, copywriting, marketing, decoration, life,
and corporate service. Second, the seeker must select the best one or several of the many
solvers to whom to offer a reward. Thus, competition exists between solvers, and the
greater the number of solvers simultaneously, the more likely it is for the works of the
winning bidder to be selected by the seeker. Augmenting the competition quality by
increasing the number of bidders can enhance platform efficiency. Finally, although not
all solvers become successful bidders, the works submitted by solvers can be shared by
everyone on the platform, contributing to the platform via user participation.
Prior studies on crowdsourcing platforms tended to explore user intentions [6,17],
whereas this study attempts to focus on the actual participation of users. Hence, we used
the ratio of the number of bidders to the number of viewers to demonstrate the users’
participation behavior.
2.1.2. Information Asymmetry and Trust
Information asymmetry transpires when “different people know different informa-
tion” [18]. As some information is private, asymmetry occurs between those who own the
information and those who do not, and those who own the information might make better
decisions. Reportedly, the information asymmetry between the two parties could result in
inefficient transactions and could lead to market failure [19]. Two types of information are
predominantly crucial for asymmetry: information about quality, and information about
intention [20]. When the solver does not comprehend the seeker’s information charac-
teristics, information asymmetry is essential. Conversely, when the seeker is concerned
about the solver’s behavior or intentions, information asymmetry is also crucial [21]. The
seeker can send observable information about itself to the less informed solver in order to
promote communication [22].
Signaling theory considers reputation to be a vital “signal” used by external stake-
holders to assess a company [23]. Reputation is defined as an assessment of the target’s
desirability established by outsiders [24]. On the e-commerce platform, reputation is the
buyer’s timely assessment of the seller’s products or services. On crowdsourcing platforms,
reputation is the assessment of the seeker received by the solver, along with its own level.
Moreover, the reward price can also be used as a signal [25]. On crowdsourcing platforms,
as the task completion party, the solver must attain the corresponding reward from the
seeker. Through reward, the solver can create an understanding of the seeker, thereby
decreasing the information asymmetry between one another.
In the sharing economy, products or services primarily rely on individuals; thus, the
sharing economy focuses more on trust between users. Gefen et al. [26] termed this “inter-
personal trust”—that is, one party feels that they can rely on the other party’s behavior, and
acquires a sense of security and comfort. On crowdsourcing platforms, from the standpoint
of the solver, the seeker’s reputation (e.g., ratings and text comments), feedback responsesSustainability 2021, 13, 6290 4 of 19
(e.g., response speed and feedback content), personal authentication, and exhibition of
characteristics all exert a positive impact on the solver’s trust [27].
2.2. Hypotheses
When two parties (individuals or companies) are exposed to dissimilar information,
signaling theory is beneficial for explaining their behavior [23]. In this study, the two
parties are the seeker and the solver. Strategic signaling implies actions taken by the
seeker to affect the solver’s views and behaviors [28]. Reportedly, information technology
features are signals of online communities that could affect the solver’s trust and participa-
tion [29]. Moreover, IT features, such as reputation mechanisms, could help the seeker to
mark their identity level and contribution to this platform [30]. Furthermore, providing
such IT features could help to enhance interpersonal trust [31] and directly increase user
participation.
Based on the existing literature, we can summarize six ways to enhance user partic-
ipation in online communities [32]: getting information, giving information, reputation
building, relationship development, recreation, and self-discovery. As mentioned earlier,
signaling reputation could serve to directly increase user participation in crowdsourcing,
because most of the seeker’s reputation comes from the previous assessment of the solver
and the seeker’s own contract volume on this platform. All of these factors are likely to
enhance the participation behavior of the solver in the first place. Hence, we hypothesize
the following:
Hypothesis 1. Online reputation positively correlates with user participation behavior.
Yao et al. [33] claimed that in the context of C2C online trading, seller reputation
systems could decrease serious information asymmetry. Thus, in the context of the crowd-
sourcing platform, the seeker’s reputation could be used to decrease the information
asymmetry. In addition, the mitigation of information asymmetry could build interper-
sonal trust among both parties [34]. Thus, we imagine that the seeker’s online reputation
could build trust between both parties, so that more solvers can participate in it. Hence,
we hypothesize the following:
Hypothesis 2. Interpersonal trust positively mediates the correlation between online reputation
and user participation behavior.
Additional methods to boost users to participate in crowdsourcing include the rewards
provided by network services. Reward is an external form of motivation to increase
participation in virtual communities [35]. Thus, unless users receive a satisfactory reward
to make up for the resources they invested, they will not participate [36]. In the buying
and selling market, as consumers are keen to get price information on the market, price
comparison services are created, through which consumers can compare the prices of
different retailers in order to make better decisions. Thus, on our crowdsourcing platform,
the solver decides whether to participate in the task by evaluating the reward for the task;
if the reward level is high, he/she might choose to participate. Hence, we hypothesize
the following:
Hypothesis 3. Salary comparison positively correlates with user participation behavior.
The signals of price comparison services closely correlate with the practicality of
information, including information asymmetry or symmetry [37]. Consumers use tools
like price-watch services to resolve the problem of information asymmetry. Moreover,
Lee et al. [38] reported that some products in the market are homogenized products that
can only be distinguished by price. If a consumer receives the price comparison, it will
lead to lower search costs, which, in turn, would decrease information asymmetry.Sustainability 2021, 13, 6290 5 of 19
In our crowdsourcing context, the solver has the right to know the salary levels of all
seekers releasing tasks in the market. Unquestionably, the solver expects that the reward for
the task in which he/she participates is above the average level. Accordingly, the solver can
decrease the search cost and evade unnecessary losses, thereby establishing interpersonal
trust between the two parties. Trust is clearly the most critical issue in determining why
users continue to use Web services [35]. Owing to the establishment of interpersonal trust,
the solver is involved in the task. Hence, we hypothesize the following:
Hypothesis 4. Interpersonal trust positively mediates the correlation between salary comparison
and user participation behavior.
Based on the existing literature, information quality captures e-commerce content
issues, and if a potential buyer or supplier wishes to initiate a transaction through the Inter-
net, the Web content should be modified, complete, relevant, and easy to comprehend [39].
The information quality of crowdsourcing platforms is primarily based on online task
information quality. Task information diversification implies that the seeker’s description
of its task is comprehensive and clear, through multiple forms such as text and pictures; this
is a manifestation of high-quality task information. Notably, task information overload is
manifested by the excessive length of a task’s description by the seeker, making the solver
burdened with reading and insufficient understanding; this is an example of low-quality
task information. Of note, online task information quality is a type of signal—such as task
information diversification and task information overload—that is recognized as a context
variable that moderates the impact of online seeker signals on user participation behavior.
Hence, we can suppose that task information diversification plays a positive role in
this, which would render the positive correlation between online seeker signals and user
participation behavior more significant. Thus, we hypothesize the following:
Hypothesis 5a. Task information diversification positively moderates (strengthens) the positive
correlation between online reputation and user participation behavior.
Hypothesis 5b. Task information diversification positively moderates (strengthens) the positive
correlation between salary comparison and user participation behavior.
Nevertheless, task information overload exerts a weakening effect on the positive
correlation between online seeker signals and user participation behavior, because it can
allow the solver to form a negative attitude toward the task, thinking that the task is too
complicated. Hence, we hypothesize the following:
Hypothesis 6a. Task information overload negatively moderates (weakens) the positive correlation
between online reputation and user participation behavior.
Hypothesis 6b. Task information overload negatively moderates (weakens) the positive correlation
between salary comparison and user participation behavior.
2.3. Theoretical Framework
Figure 1 presents the theoretical framework of the research, followed by four main
hypotheses and four subhypotheses.Sustainability 2021, 13, x FOR PEER REVIEW
Sustainability 2021, 13, 6290 6 of 19
Task Information
Online Seeker Signals Diversification (TID)
Online Reputation (OR) H5a
H1 H5b
H2
Interpersonal User Participation
Trust (IT) Behavior (UPB)
H4
H6b
H3 H6a
Salary Comparison (SC)
Task Information
Overload (TIO)
Control Variables
Number of Visitors (NV) Duration of the Task (DT)
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
3. Material and Methods
3.1. Research Context and Data
3. Material andCollection
Methods
This study3.1.
tested the proposed
Research Context andresearch hypotheses with transaction data collected
Data Collection
from one of China’sThis threestudy tested the proposed and
innovative knowledge skillshypotheses
research sharing service platforms, data coll
with transaction
EPWK.com. Asfrom of the end of June 2020, this platform has had over
one of China’s three innovative knowledge and skills sharing 22 million registered
service platfo
users. In addition, the data on
EPWK.com. Asthe website,
of the end ofboth
Junepast andthis
2020, present, are relatively
platform public,
has had over and
22 million regis
the task modelsusers.
are diverse. Overall, we selected this website as the data source.
In addition, the data on the website, both past and present, are relatively pu
The crowdsourcing
and the task platform
models inarethis study
diverse. contained
Overall, six different
we selected task models,
this website as source
as the data
follows: “single reward”; “multiperson reward”; “tendering task”; “employment
The crowdsourcing platform in this study contained six different task mode task”;
“piece-rate reward”; and“single
follows: “directreward”;
employment.” The number
“multiperson of bidders
reward”; for direct
“tendering task”;employ-
“employment t
ment tasks was“piece-rate
one, whichreward”;
was determined
and “direct employment.” The numbermodels
by the seeker. The other task werefor direct
of bidders
multiperson bidding, in which the seeker selected the best task works.
ployment tasks was one, which was determined by the seeker. The other In this study, we task mo
used the Web crawling tool Pythonbidding,
were multiperson to collectindata
which between June 30
the seeker 2010 and
selected theDecember
best task31 works. In
2019. Meanwhile, in order to ensure data integrity, only “completed” tasks
study, we used the Web crawling tool Python to collect data between June 30 2010were selected
for data collection. After data
December cleaning,
31 2019. sample in
Meanwhile, data of 110,000
order tasks
to ensure were
data obtained.
integrity, onlyOwing
“completed”
to the lack of data in the direct employment task, there was no way to perform
were selected for data collection. After data cleaning, sample data of 110,000 tasks a compre-
hensive analysis of this type
obtained. Owingof task andlack
to the delete it. Finally,
of data in the we obtained
direct 28,887 valid
employment task, sample
there was no w
data, and theseperform
data were standardized. Examples of raw data before standardization
a comprehensive analysis of this type of task and delete it. Finally, are we obta
shown in Table28,887
1. valid sample data, and these data were standardized. Examples of raw dat
fore standardization are shown in Table 1.
3.2. Variables and Overall Approach
3.2.1. Variables Table 1. Examples of raw data before standardization.
Table 2 shows all of the variables used in this study. The dependent variable was the
User Partici- Task Infor- Task In-
user participation
Online behavior,
Salary which was equal to the ratio of the number of solversNumber
Interper- for a task
of Durati
Task ID Task Model pation Be- mation Di- formation
to the number of visitors for a task. The higher the ratio, the more solvers were involved in
Reputation Comparison sonal Trust Visitors the T
havior versification Overload
the task’s creation, which would create more works, bring more choices to the seekers, and
Multiperson
1078 9 with everyone
also share ideas 0.418 4
involved. 0.112
In addition, 0
the independent 0
variables 322
included 10
reward
online reputation and salary comparison. The mediating variable was interpersonal trust;
Piece-rate
1833 9
the trust between two 0.731 5
parties increased 0.151 of solvers1 from different
the number 0 696
credit ratings. 10
reward
16170 Single The moderating
reward 4 variables
0.754included 9task information
0.041 diversification
2 and0task information
977 7
overload.
Employment Most of the variables were shown directly on the task release homepage and the
26128 4 1.009 8
seeker’s homepage,
task and could be seen by the 0.015
solvers. 6 0 1104 15Sustainability 2021, 13, 6290 7 of 19
Table 1. Examples of raw data before standardization.
User Participation Task Information Task Information Duration of
Task ID Task Model Online Reputation Salary Comparison Interpersonal Trust Number of Visitors
Behavior Diversification Overload the Task
Multiperson
1078 9 0.418 4 0.112 0 0 322 10
reward
1833 Piece-rate reward 9 0.731 5 0.151 1 0 696 10
16170 Single reward 4 0.754 9 0.041 2 0 977 7
26128 Employment task 4 1.009 8 0.015 6 0 1104 15
94836 Tendering task 9 0.300 7 0.005 0 2557 6303 1
Table 2. Constructs and measurement.
Variable Type Constructs Variable Definitions Measurement Methods
Online reputation
Credit Credit rating of the seeker on the seeker’s homepage
Independent variables (OR)
Salary comparison
Task similarity–salary difference The ratio of the task price to the average price of similar tasks
(SC)
Mediating
Interpersonal trust (IT) Credit diversification The number of solvers from different credit ratings of all solvers
variables
Dependent User participation behavior The ratio of the number of solvers for a task to the number of
Bid ratio
variables (UPB) visitors for a task
Task information diversification The number of attachment contents, which represents other
Number of attachment contents
Moderating variables (TID) forms in addition to the text on the task release homepage
Task information overload
Bytes of attachment text The bytes of attachment text on the task release homepage
(TIO)
Control variables Number of visitors (NV) Number of visitors The number of visitors for a task
Duration of the task (DT) Duration of the task The number of days from task release to acceptance of paymentSustainability 2021, 13, 6290 8 of 19
Online reputation represented the seeker’s trust level on his/her homepage, which
is measured by the seeker’s credit rating. Salary comparison demonstrates the level of
reward for the solvers; this is measured by salary differences between similar tasks. Table 2
shows the specific calculation method.
Task information diversification implies diverse descriptions of different forms of task
information by the seeker; this is measured by the number of attachment contents, which
represents other forms in addition to the text on the task release homepage. Both task
requirements and supplementary requirements describe the seeker’s request for his/her
task in the form of text. Some tasks would also include attachments. The attachments
contain text-like and other forms of task descriptions. Therefore, what we are concerned
about is the number of other forms of task description. In addition, task information
overload is measured by the bytes of attachment text on the task release homepage. If
the number of bytes in the task description is too high, it will cause a certain amount of
information overload to the solver.
Control variables include the number of visitors and the duration of the task. The
visitors to the task could become solvers or not. The duration of the task is the number of
days from the release of requirements to the acceptance of payment.
Table 3 presents the descriptive statistics. Table 4 reports the correlations between key
variables. Of note, multicollinearity might not be a major issue for this study.
Table 3. Descriptive statistics.
Variable Obs Min Median Mean Max Std. Dev.
User participation behavior 28,887 0.000 0.145 0.266 10.000 0.343
Online reputation 28,887 3.010 9.542 9.254 10.000 1.162
Salary comparison 28,887 0.000 1.085 1.085 9.189 0.682
Interpersonal trust 28,887 3.010 9.031 8.311 10.000 1.593
Task information diversification 28,887 0.000 0.000 0.715 10.000 1.347
Task information overload 28,887 0.000 0.000 0.366 10.000 1.174
Number of visits 28,887 0.786 4.520 4.533 10.000 0.515
Duration of the task 28,887 0.000 4.177 4.068 10.000 1.520
Table 4. Correlations between key variables.
Variables 1 2 3 4 5 6 7
UPB
OR 0.031 ***
SC 0.13 *** −0.22 ***
IT 0.229 *** −0.027 *** 0.231 ***
TID −0.042 *** 0.005 0.004 −0.096 ***
TIO −0.1 *** −0.032 *** −0.003 −0.065 *** 0.006
NV −0.321 *** 0.176 *** 0.067 *** 0.087 *** −0.053 *** 0.083 ***
DT 0.129 *** −0.054 *** 0.214 *** 0.385 *** 0.036 *** −0.031 *** 0.083 ***
Note: *** indicate significance at the levels of 1%, respectively.
3.2.2. Overall Approach
In this study, we used the system of regression approach and SEM analysis for com-
parative analysis. This article follows the guidelines of quantitative models [40]. AMOS–
LISREL-type search algorithms provide fitting algorithms, but tend to work well only for
normally distributed survey data. However, the data used in this article do not conform to
the multivariate normal distribution. Therefore, this article chooses the system of regression
approach and SEM analysis, which have well-understood fitting measures. Furthermore,
we used Stata 15 and SmartPLS 3 software for our analysis.Sustainability 2021, 13, 6290 9 of 19
4. Empirical Models and Data Results
4.1. Adoption of Independent Variables and User Participation Behavior
Equations (1) and (2) outline our empirical models for Hypotheses 1 and 3. We index
the task by i. In all equations, we controlled the duration of the task and the number of
visits. We conducted OLS regressions in order to estimate Equations (1) and (2)—OLS:1 and
OLS:2, respectively. Columns 2 and 3 of Table 5 show the OLS regression results. Column
2 adds the influence of the online reputation on the user participation behavior. Column 3
adds the influence of the salary comparison on the user participation behavior.
Table 5. Estimation results for user participation behavior.
OLS:1 OLS:2
Dependent Variable
UPB UPB
Constant 0.901 ***(0.021) 1.104 ***(0.017)
Control variables
NV −0.235 ***(0.004) −0.226 ***(0.004)
DT 0.037 ***(0.001) 0.029 ***(0.001)
Independent variables
OR 0.03 ***(0.002)
SC 0.063 ***(0.003)
Model summary
No. of Obs. 28,887 28,887
Adj-R2 0.1369 0.1417
R2 0.137 0.1418
F 1528.69 *** 1591.21 ***
Mean VIF 1.03 1.04
(1) Standard errors are reported in parentheses; (2) *** indicate significance at the levels of 1%, respectively.
Based on the results shown in column 2, we find that there is a significant positive cor-
relation between online reputation and user participation behavior (β = 0.03, p < 0.01). Thus,
Hypothesis 1, which predicts a positive effect of online reputation on user participation
behavior, is supported. Meanwhile, we also observed a positive and significant correlation
between salary comparison and user participation behavior (β = 0.063, p < 0.01). Thus,
Hypothesis 3, which predicts a positive effect of salary comparison on user participation
behavior, is supported.
UPBi = β 0 + β 1 × ORi + β 2 × NVi + β 3 × DTi + ε i (1)
UPBi = β 0 + β 1 × SCi + β 2 × NVi + β 3 × DTi + ε i (2)
4.2. Mediating Effect Analysis
Equations (1)–(6) outline our empirical models for Hypothesis 2 and Hypothesis 4. We
index the task by i. In all equations, we controlled the duration of the task and the number
of visits. We conducted OLS regressions in order to estimate Equations (1)–(6). Columns
2–7 of Table 6 show the OLS regression results. Columns 2–4 add the mediating effect of
interpersonal trust on the relationship between online reputation and user participation
behavior. Columns 5–7 add the mediating effect of interpersonal trust on the relationship
between salary comparison and user participation behavior.
Based on the results shown in column 2, we observed a positive and significant
correlation between online reputation and user participation behavior (β = 0.03, p < 0.01).
In column 3, we observed a negative and significant correlation between online reputation
and interpersonal trust (β = −0.022, p < 0.01). In column 4, we observed a positive and
significant correlation between online reputation and user participation behavior (β = 0.031,
p < 0.01), and a positive and significant correlation between interpersonal trust and user
participation behavior (β = 0.051, p < 0.01). Thus, we can conclude that interpersonal
trust plays a mediating role between online reputation and user participation behavior.Sustainability 2021, 13, 6290 10 of 19
Since the coefficient of OLS:3 corresponding to online reputation is −0.022, which is a
negative number, this indicates that there is a negative mediating effect. Thus, Hypothesis 2,
which predicts a positive mediating role between online reputation and user participation
behavior, is not supported.
Table 6. Estimation results for the mediating effects of interpersonal trust.
Dependent OLS:1 OLS:3 OLS:4 OLS:2 OLS:5 OLS:6
Variable UPB IT UPB UPB IT UPB
Constant 0.901 ***(0.021) 6.075 ***(0.097) 0.593 ***(0.022) 1.104 ***(0.017) 5.76 ***(0.077) 0.834 ***(0.018)
Control
variables
−0.235 −0.244 −0.226 −0.233
NV 0.182 ***(0.017) 0.149 ***(0.017)
***(0.004) ***(0.004) ***(0.004) ***(0.004)
DT 0.037 ***(0.001) 0.398 ***(0.006) 0.017 ***(0.001) 0.029 ***(0.001) 0.365 ***(0.006) 0.012 ***(0.001)
Independent
variables
−0.022
OR 0.03 ***(0.002) 0.031 ***(0.002)
***(0.008)
SC 0.063 ***(0.003) 0.358 ***(0.013) 0.046 ***(0.003)
Mediating
variable
IT 0.051 ***(0.001) 0.047 ***(0.001)
Model
summary
No. of Obs. 28,887 28,887 28,887 28,887 28,887 28,887
Adj-R2 0.1369 0.1516 0.184 0.1417 0.1737 0.1809
R2 0.137 0.1517 0.1841 0.1418 0.1738 0.181
F 1528.69 *** 1721.08 *** 1629 *** 1591.21 *** 2025.33 *** 1596.26 ***
Mean VIF 1.03 1.03 1.11 1.04 1.04 1.13
(1) Standard errors are reported in parentheses; (2) *** indicate significance at the levels of 1%, respectively.
In this study, we performed the Sobel and bootstrapping tests using Stata software;
the results are shown in Table 7. The indirect effect of Hypothesis 2 is −0.001, the direct
effect of Hypothesis 2 is 0.031, and the total effect of Hypothesis 2 is 0.030. The p value
of the Sobel test of the mediation effect is 0.003, which is less than 0.05, indicating that
the mediation effect is established. The mediation effect accounts for −3.75% of the total
effect—that is, interpersonal trust has a mediating effect between online reputation and
user participation behavior, and there is a certain negative mediation. The results were
contrary to the prediction of Hypothesis 2. Therefore, as shown in Table 7, Hypothesis 2 is
not supported.
Table 7. Significance analysis of the direct and indirect effects by regression analysis.
Total Significance Direct Significance Indirect Significance Hypothesis Is
Z Value Z Value Z Value
Effect (p < 0.05) Effect (p < 0.05) Effect (p < 0.05) Supported
Hypothesis 2 0.030 18.282 YES 0.031 19.504 YES −0.001 −2.930 YES NO
Hypothesis 4 0.063 22.315 YES 0.046 16.496 YES 0.017 22.361 YES YES
Based on the results shown in column 5 of Table 6, we observed a positive and signif-
icant correlation between salary comparison and user participation behavior (β = 0.063,
p < 0.01). In column 6, we observed a positive and significant correlation between salary
comparison and interpersonal trust (β = 0.358, p < 0.01). In column 7, we observed a
positive and significant correlation between salary comparison and user participation be-
havior (β = 0.046, p < 0.01), and a positive and significant correlation between interpersonal
trust and user participation behavior (β = 0.047, p < 0.01). Thus, we can conclude that
interpersonal trust plays a mediating role between salary comparison and user participa-Sustainability 2021, 13, 6290 11 of 19
tion behavior. Since the coefficient of OLS:5 corresponding to salary comparison is 0.358,
which is a positive number, this indicates that there is a positive mediating effect. Thus,
Hypothesis 4, which predicts a positive mediating role between salary comparison and
user participation behavior, is supported.
In this study, we performed the Sobel and bootstrapping tests using Stata software;
the results are shown in Table 7. The indirect effect of Hypothesis 4 is 0.017, the direct effect
of Hypothesis 4 is 0.046, and the total effect of Hypothesis 4 is 0.063. The P value of the
Sobel test of the mediation effect is 0, which is less than 0.05, indicating that the mediation
effect is established. The mediation effect accounts for 26.8% of the total effect—that is,
interpersonal trust has a mediating effect between salary comparison and user participation
behavior, and there is a certain positive mediation. These results support Hypothesis 4, as
shown in Table 7.
ITi = β 0 + β 1 × ORi + β 2 × NVi + β 3 × DTi + ε i (3)
UPBi = β 0 + β 1 × ORi + β 2 × ITi + β 3 × NVi + β 4 × DTi + ε i (4)
ITi = β 0 + β 1 × SCi + β 2 × NVi + β 3 × DTi + ε i (5)
UPBi = β 0 + β 1 × SCi + β 2 × ITi + β 3 × NVi + β 4 × DTi + ε i (6)
In order to further verify the mediating effects of Hypothesis 2 and Hypothesis 4, we
also performed the Sobel and bootstrapping tests using SmartPLS software; the results
are shown in Table 8. The indirect effect of Hypothesis 2 is 0.005, while the direct effect of
Hypothesis 2 is 0.143. The P value of the Sobel test of the mediation effect is 0, which is less
than 0.05, indicating that the mediation effect is established—that is, interpersonal trust has
a mediating effect between online reputation and user participation behavior, and there
is a certain positive mediation. These results support Hypothesis 2, as shown in Table 8.
The indirect effect of Hypothesis 4 is 0.049, while the direct effect of Hypothesis 4 is 0.173.
The p value of the Sobel test of the mediation effect is 0, which is less than 0.05, indicating
that the mediation effect is established—that is, interpersonal trust has a mediating effect
between salary comparison and user participation behavior, and there is a certain positive
mediation. These results support Hypothesis 4, as shown in Table 8.
Table 8. Significance analysis of the direct and indirect effects by SEM analysis.
95% 95%
Hypothesis
Direct Confidence Significance Indirect Confidence Significance
t Value t Value Is
Effect Interval of the (p < 0.05) Effect Interval of the (p < 0.05)
Supported
Direct Effect Indirect Effect
Hypothesis 2 0.143 (0.131. 0.153) 25.843 YES 0.005 (0.003, 0.008) 4.157 YES YES
Hypothesis 4 0.173 (0.162, 0.185) 29.718 YES 0.049 (0.045. 0.053) 23.436 YES YES
4.3. Moderating Effect Analysis
Equations (1)–(2) and (7)–(10) outline our empirical models for Hypotheses 5a and
5b. We index the task by i. In all equations, we controlled the duration of the task and the
number of visits. We conducted OLS regressions in order to estimate Equations (1)–(2) and
(7)–(10). Columns 2–7 of Table 9 show the OLS regression results. Columns 2–4 add the
moderating effect of task information diversification on the relationship between online
reputation and user participation behavior. Columns 5–7 add the moderating effect of
task information diversification on the relationship between salary comparison and user
participation behavior.Sustainability 2021, 13, 6290 12 of 19
Table 9. Estimation results for the moderating effects of task information diversification.
Dependent OLS:1 OLS:7 OLS:8 OLS:2 OLS:9 OLS:10
Variable UPB UPB UPB UPB UPB UPB
Constant 0.901 ***(0.021) 0.92 ***(0.021) 0.948 ***(0.022) 1.104 ***(0.017) 1.125 ***(0.017) 1.13 ***(0.017)
Control
variables
−0.235 −0.237 −0.236 −0.226 −0.229 −0.229
NV
***(0.004) ***(0.004) ***(0.004) ***(0.004) ***(0.004) ***(0.004)
DT 0.037 ***(0.001) 0.038 ***(0.001) 0.038 ***(0.001) 0.029 ***(0.001) 0.03 ***(0.001) 0.03 ***(0.001)
Independent
variables
OR 0.03 ***(0.002) 0.03 ***(0.002) 0.027 ***(0.002)
SC 0.063 ***(0.003) 0.063 ***(0.003) 0.059 ***(0.003)
Moderator
variable
−0.017 −0.087 −0.017 −0.022
TID
***(0.001) ***(0.013) ***(0.001) ***(0.003)
TIO
Interactions
OR × TID 0.008 ***(0.001)
OR × TIO
SC × TID 0.005 ***(0.002)
SC × TIO
Model
summary
No. of Obs. 28,887 28,887 28,887 28,887 28,887 28,887
Adj-R2 0.1369 0.1414 0.1422 0.1417 0.1459 0.1461
R2 0.137 0.1415 0.1423 0.1418 0.146 0.1462
F 1528.69 *** 1189.95 *** 958.34 *** 1591.21 *** 1234.91 *** 989.19 ***
Mean VIF 1.03 1.03 38.11 1.04 1.03 2.26
(1) Standard errors are reported in parentheses; (2) *** indicate significance at the levels of 1%, respectively.
According to column 4, online reputation and task information diversification have a
strongly positively significant interaction effect on user participation behavior (β = 0.008,
p < 0.01). Since the ANOVA test is significant (p = 1.563 × 10−7 < 0.05), it can be concluded
that there is a significant difference between Equation (7) and Equation (8), and that task
information diversification has a positive moderating effect [41]. Thus, Hypothesis 5a,
which predicts a positive moderating role between online reputation and user participation
behavior, is supported.
According to column 7, salary comparison and task information diversification have a
strongly positively significant interaction effect on user participation behavior (β = 0.005,
p < 0.01). Since the ANOVA test is significant (p = 0.018 < 0.05), it can be concluded that
there is a significant difference between Equation (9) and Equation (10), and that task infor-
mation diversification has a positive moderating effect [41]. Thus, Hypothesis 5b, which
predicts a positive moderating role between salary comparison and user participation
behavior, is supported.
UPBi = β 0 + β 1 × ORi + β 2 × TIDi + β 3 × NVi + β 4 × DTi + ε i (7)
UPBi = β 0 + β 1 × ORi + β 2 × TIDi + β 3 × ORi × TIDi + β 4 × NVi + β 5 × DTi + ε i (8)
UPBi = β 0 + β 1 × SCi + β 2 × TIDi + β 3 × NVi + β 4 × DTi + ε i (9)
UPBi = β 0 + β 1 × SCi + β 2 × TIDi + β 3 × SCi × TIDi + β 4 × NVi + β 5 × DTi + ε i (10)
Equations (1), (2) and (11)–(14) outline our empirical models for Hypotheses 6a and
6b. We index the task by i. In all equations, we controlled the duration of the task and
the number of visits. We conducted OLS regressions in order to estimate Equations (1), (2)
and (11)–(14). Columns 2–7 in Table 10 show the OLS regression results. Columns 2–4Sustainability 2021, 13, 6290 13 of 19
add the moderating effect of task information overload on the relationship between on-
line reputation and user participation behavior. Columns 5–7 add the moderating effect
of task information overload on the relationship between salary comparison and user
participation behavior.
Table 10. Estimation results for the moderating effects of task information overload.
Dependent OLS:1 OLS:11 OLS:12 OLS:2 OLS:13 OLS:14
Variable UPB UPB UPB UPB UPB UPB
Constant 0.901 ***(0.021) 0.901 ***(0.021) 0.881 ***(0.022) 1.104 ***(0.017) 1.096 ***(0.017) 1.091 ***(0.017)
Control
variables
−0.235 −0.231 −0.231 −0.226 −0.223 −0.223
NV
***(0.004) ***(0.004) ***(0.004) ***(0.004) ***(0.004) ***(0.004)
DT 0.037 ***(0.001) 0.036 ***(0.001) 0.036 ***(0.001) 0.029 ***(0.001) 0.029 ***(0.001) 0.029 ***(0.001)
Independent
variables
OR 0.03 ***(0.002) 0.029 ***(0.002) 0.031 ***(0.002)
SC 0.063 ***(0.003) 0.063 ***(0.003) 0.068 ***(0.003)
Moderator
variable
TID
−0.019 −0.008
TIO 0.029 ***(0.012) −0.02 ***(0.002)
***(0.002) ***(0.003)
Interactions
OR × TID
−0.005
OR × TIO
***(0.001)
SC × TID
−0.011
SC × TIO
***(0.002)
Model
summary
No. of Obs. 28,887 28,887 28,887 28,887 28,887 28,887
Adj-R2 0.1369 0.1409 0.1414 0.1417 0.1464 0.1472
R2 0.137 0.141 0.1415 0.1418 0.1465 0.1474
F 1528.69 *** 1185.49 *** 952.29 *** 1591.21 *** 1239.25 ** 998.38 ***
Mean VIF 1.03 1.03 21.89 1.04 1.03 1.84
(1) Standard errors are reported in parentheses; (2) *** and ** indicate significance at the levels of 1% and 5%, respectively.
According to column 4, online reputation and task information overload have a
strongly negatively significant interaction effect on user participation behavior (β = −0.005,
p < 0.01). Since the ANOVA test is significant (p = 4.027 × 10−5 < 0.05), it can be concluded
that there is a significant difference between Equation (11) and Equation (12), and that task
information overload has a negative moderating effect [41]. Thus, Hypothesis 6a, which
predicts a negative moderating role between online reputation and user participation
behavior, is supported.
According to column 7, salary comparison and task information overload have a
strongly negatively significant interaction effect on user participation behavior (β = −0.011,
p < 0.01). Since the ANOVA test is significant (p = 4.502 × 10−8 < 0.05), it can be concluded
that there is a significant difference between Equation (13) and Equation (14), and that task
information overload has a negative moderating effect [41]. Thus, Hypothesis 6b, which
predicts a negative moderating role between salary comparison and user participation
behavior, is supported.
UPBi = β 0 + β 1 × ORi + β 2 × TIOi + β 3 × NVi + β 4 × DTi + ε i (11)
UPBi = β 0 + β 1 × ORi + β 2 × TIOi + β 3 × ORi × TIOi + β 4 × NVi + β 5 × DTi + ε i (12)Sustainability 2021, 13, 6290 14 of 19
UPBi = β 0 + β 1 × SCi + β 2 × TIOi + β 3 × NVi + β 4 × DTi + ε i (13)
UPBi = β 0 + β 1 × SCi + β 2 × TIOi + β 3 × SCi × TIOi + β 4 × NVi + β 5 × DTi + ε i (14)
In order to further verify Hypotheses 5 and 6, this article uses SEM analysis in order
to test the moderating effects, and the results are shown in Figures 2–5. The three lines
shown in Figures 2 and 4 represent the correlation between online reputation and user
participation behavior, while Figures 3 and 5 represent the correlation between salary
comparison and user participation behavior. The middle line signifies the correlation
for average levels of the moderator variables task information diversification and task
information overload [42]. The other two lines depict the relationship between the x-axis
and the y-axis for higher and lower levels of the moderator variables task information
diversification and task information overload.
Figures 2 and 3 show that the correlation between online reputation and user participa-
tion behavior, along with the correlation between salary comparison and user participation
behavior, are positive in the middle line. Thus, higher levels of online reputation would
cause higher levels of user participation behavior, and higher levels of salary comparison
would cause higher levels of user participation behavior. Moreover, higher task infor-
mation diversification entails a stronger correlation between online reputation and user
participation behavior, whereas lower levels of task information diversification build a
weaker correlation between online reputation and user participation behavior. Hence,
the simple slope plot supports our previous discussion of the positive interaction terms
of Hypothesis 5a. Meanwhile, higher task information diversification entails a stronger
correlation between salary comparison and user participation behavior, whereas lower
levels of task information diversification result in a weaker correlation between salary
comparison and user participation behavior. Hence, the simple slope plot supports our
previous discussion of the positive interaction terms of Hypothesis 5b. Figures 4 and
5 show that the correlation between online reputation and user participation behavior,
along with the correlation between salary comparison and user participation behavior, are
positive in the middle line. Thus, higher levels of online reputation would cause higher
levels of user participation behavior, and higher levels of salary comparison would cause
higher levels of user participation behavior. Moreover, higher task information overload
entails a weaker correlation between online reputation and user participation behavior,
whereas lower levels of task information overload build a stronger correlation between
online reputation and user participation behavior. Hence, the simple slope plot supports
our previous discussion of the negative interaction terms of Hypothesis 6a. Meanwhile,
higher task information overload entails a weaker correlation between salary comparison
and user participation behavior, whereas lower levels of task information overload result in
a stronger correlation between salary comparison and user participation behavior. Hence,
Sustainability 2021, 13, x FOR PEER REVIEW 14 of 19
the simple slope plot supports our previous discussion of the negative interaction terms of
Hypothesis 6b.
Figure 2.
Figure 2. Moderating
Moderatingeffect simple
effect slope
simple analysis
slope for Hypothesis
analysis 5a.
for Hypothesis 5a.Sustainability 2021, 13, 6290 15 of 19
Figure 2. Moderating effect simple slope analysis for Hypothesis 5a.
Sustainability 2021, 13, x FOR PEER REVIEW 15 of 19
Sustainability 2021, 13, x FOR PEER REVIEW 15 of 19
Figure 3.
Figure 3. Moderating
Moderatingeffect simple
effect slope
simple analysis
slope for Hypothesis
analysis 5b.
for Hypothesis 5b.
Figures 4 and 5 show that the correlation between online reputation and user par-
ticipation behavior, along with the correlation between salary comparison and user par-
ticipation behavior, are positive in the middle line. Thus, higher levels of online reputa-
tion would cause higher levels of user participation behavior, and higher levels of salary
comparison would cause higher levels of user participation behavior. Moreover, higher
task information overload entails a weaker correlation between online reputation and
user participation behavior, whereas lower levels of task information overload build a
stronger correlation between online reputation and user participation behavior. Hence,
the simple slope plot supports our previous discussion of the negative interaction terms
of Hypothesis 6a. Meanwhile, higher task information overload entails a weaker correla-
tion between salary comparison and user participation behavior, whereas lower levels of
task information overload result in a stronger correlation between salary comparison
and user participation behavior. Hence, the simple slope plot supports our previous
discussion
Figure
Figure 4. of the negative
4. Moderating
Moderating effect interaction
simple
effect slope
simple terms
analysis
slope of
forHypothesis
analysis Hypothesis 6b. 6a.
6a.
for Hypothesis
Figure 4. Moderating effect simple slope analysis for Hypothesis 6a.
Figure 5. Moderating effect simple slope analysis for Hypothesis 6b.
Figure 5.
Figure 5. Moderating
Moderatingeffect simple
effect simple slope analysis
slope for Hypothesis
analysis for Hypothesis6b. 6b.
5. Discussion
5. Discussion
5. Discussion
5.1. Conclusions
5.1. Conclusions
5.1. Conclusions
This study suggests that online seeker signals, including online reputation and sal-
This study
This studysuggests
ary comparison, suggests that
could affect that online
useronline seeker
seeker
participationsignals, including
signals,
behavior, and online
including thatonlinereputation
online seekerand
reputation sal-
signalsand salary
ary comparison,
comparison,
could also positivelycould
could affect
affect
affect useruser
user participationbehavior
participation
participation behavior,
behavior, and
byand that
that online
building online seeker
seekersignals
interpersonal signals
trust. could
couldpositively
also online
Moreover,
also positively
task affect
affect useruser
information participation
quality, including
participation behavior
behavior byby
task building interpersonal
information
building interpersonal
diversificationtrust.and More-
trust.
Moreover, online
task information task information quality, including task information diversification and
over, online taskoverload,
information play aquality,
moderating role between
including online seeker
task information signals and user
diversification and task
task information
participation overload,
behavior. play a moderating role between online seeker signals and user
information overload, play a moderating role between online seeker signals and user
participation
Of note, behavior.
some hypothetical results corroborate our expectations (Hypothesis 1, Hy-
participation
Of note,
behavior.
some hypothetical results corroborate
pothesis 3, and Hypothesis 4). Online reputation canour expectations
exert (Hypothesis
a positive impact on user 1, par-
Hy-
Of note,
pothesis 3, and some hypothetical
Hypothesis 4). Online results corroborate
reputation can exert our
a expectations
positive impact (Hypothesis
on user par- 1, Hy-
ticipation behavior. The seeker’s reputation is derived from the seeker’s own credit,
pothesis
ticipation 3, and
behavior.Hypothesis 4). Online reputation can exert a positive impact on user
which in turn derivesThefrom seeker’s
his/herreputation
performance. is derived
However, fromthe the seeker’sofown
conclusion this credit,
article
participation
which in
shows turn
that
behavior.
derives from
interpersonal
The seeker’s
his/her
trust plays
reputation
performance. is derived
However, the
a negative mediating
from
roleconclusion
the seeker’s own
of this article
in the relationship be-
credit,
which
tween in
shows online
thatturn derives from
interpersonal
reputation andtrust his/her
userplays aperformance.
negative
participation However,
mediating
behavior, role inthe
as demonstratedtheconclusion
relationship of be-
by the regres- this article
shows
sion analysis. The reason for this may be that for those solvers with different credit rat-between
tween that
online interpersonal
reputation and trust
user plays a negative
participation mediating
behavior, as role in
demonstrated the relationship
by the regres-
sion analysis.
online
ings, reputation
the seeker'sThecredit
reason is for
and user this may to
notparticipation
enough begenerate
that for those
behavior, assolvers
sufficient with
demonstrated
trust. different
Thus, webyneed
thecredit rat-
regression
to con- anal-
ings, the seeker's credit is not enough to generate sufficient trust.
sider the online reputation of other forms of seeker in the later stage, in order to increaseThus, we need to con-
sider
the the of
trust online reputation
the solver. of other
However, forms of
through SEMseeker in thethe
analysis, later stage, inresults
empirical order toof increase
the me-
the trust of the solver. However, through SEM analysis,
diating effects supported Hypothesis 2; thus, Hypothesis 2 may be controversial,the empirical results of the
andme- we
diating effects supported Hypothesis
will conduct in-depth analysis on it in the future. 2; thus, Hypothesis 2 may be controversial, and we
will conduct
Furthermore,in-depth analysis
salary on it in exerts
comparison the future.
a positive impact on user participation be-You can also read
